Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations4528
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory972.8 KiB
Average record size in memory220.0 B

Variable types

DateTime1
Numeric13
Categorical2

Alerts

Bollinger_Lower is highly overall correlated with Bollinger_Middle and 5 other fieldsHigh correlation
Bollinger_Middle is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
Bollinger_Upper is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
MACD is highly overall correlated with MACD_Signal and 1 other fieldsHigh correlation
MACD_Diff is highly overall correlated with RSIHigh correlation
MACD_Signal is highly overall correlated with MACD and 1 other fieldsHigh correlation
RSI is highly overall correlated with MACD and 2 other fieldsHigh correlation
close is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
high is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
low is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
open is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
split_ratio is highly imbalanced (99.4%) Imbalance
Ticker is uniformly distributed Uniform
MACD has unique values Unique
MACD_Signal has unique values Unique
MACD_Diff has unique values Unique
dividend has 4456 (98.4%) zeros Zeros

Reproduction

Analysis started2025-02-02 16:47:59.207174
Analysis finished2025-02-02 16:48:34.590947
Duration35.38 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

date
Date

Distinct2264
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size212.2 KiB
Minimum2015-01-02 00:00:00
Maximum2023-12-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-02T11:48:34.791061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:35.057048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

open
Real number (ℝ)

High correlation 

Distinct3086
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.748655
Minimum25.17
Maximum85.900002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.8 KiB
2025-02-02T11:48:35.364573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25.17
5-th percentile31.129999
Q139.02625
median49.790001
Q360.309999
95-th percentile73.175497
Maximum85.900002
Range60.730001
Interquartile range (IQR)21.28375

Descriptive statistics

Standard deviation13.438346
Coefficient of variation (CV)0.26480202
Kurtosis-0.72066237
Mean50.748655
Median Absolute Deviation (MAD)10.700001
Skewness0.2966278
Sum229789.91
Variance180.58915
MonotonicityNot monotonic
2025-02-02T11:48:35.594660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 6
 
0.1%
48.09999847 6
 
0.1%
55 6
 
0.1%
49.11999893 5
 
0.1%
44.59999847 5
 
0.1%
45 5
 
0.1%
35 5
 
0.1%
56.84000015 5
 
0.1%
43.59999847 5
 
0.1%
67.16000366 5
 
0.1%
Other values (3076) 4475
98.8%
ValueCountFrequency (%)
25.17000008 1
< 0.1%
26.43000031 1
< 0.1%
27.07500076 1
< 0.1%
27.21500015 1
< 0.1%
27.31999969 1
< 0.1%
27.40999985 1
< 0.1%
27.43499947 1
< 0.1%
27.5 1
< 0.1%
27.52499962 1
< 0.1%
27.53499985 1
< 0.1%
ValueCountFrequency (%)
85.90000153 1
< 0.1%
85.83999634 1
< 0.1%
85.13999939 1
< 0.1%
85.12999725 1
< 0.1%
85.01000214 1
< 0.1%
84.80999756 1
< 0.1%
84.34999847 1
< 0.1%
84.33000183 1
< 0.1%
84.23999786 1
< 0.1%
84.09999847 1
< 0.1%

high
Real number (ℝ)

High correlation 

Distinct3141
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.240256
Minimum26.99
Maximum86.739998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.8 KiB
2025-02-02T11:48:35.824661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.99
5-th percentile31.402001
Q139.474999
median50.200001
Q360.990002
95-th percentile73.660004
Maximum86.739998
Range59.749998
Interquartile range (IQR)21.515002

Descriptive statistics

Standard deviation13.552011
Coefficient of variation (CV)0.26447977
Kurtosis-0.72346901
Mean51.240256
Median Absolute Deviation (MAD)10.77
Skewness0.29749709
Sum232015.88
Variance183.65701
MonotonicityNot monotonic
2025-02-02T11:48:36.048781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.95999908 6
 
0.1%
60.81000137 5
 
0.1%
53.36000061 5
 
0.1%
45.65000153 5
 
0.1%
35.52999878 5
 
0.1%
54.43000031 5
 
0.1%
46.68000031 5
 
0.1%
47.13999939 5
 
0.1%
36.97499847 5
 
0.1%
49.18999863 5
 
0.1%
Other values (3131) 4477
98.9%
ValueCountFrequency (%)
26.98999977 1
< 0.1%
27.36000061 1
< 0.1%
27.43499947 1
< 0.1%
27.55999947 1
< 0.1%
27.57999992 1
< 0.1%
27.67499924 1
< 0.1%
27.84000015 1
< 0.1%
27.84499931 1
< 0.1%
27.97500038 1
< 0.1%
27.98999977 1
< 0.1%
ValueCountFrequency (%)
86.73999786 1
< 0.1%
86.25 1
< 0.1%
86.23999786 1
< 0.1%
86.09999847 1
< 0.1%
86.08000183 1
< 0.1%
85.91999817 1
< 0.1%
85.68000031 1
< 0.1%
84.94999695 1
< 0.1%
84.83000183 1
< 0.1%
84.77999878 1
< 0.1%

low
Real number (ℝ)

High correlation 

Distinct3139
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.252724
Minimum23.07
Maximum85.269997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.8 KiB
2025-02-02T11:48:37.647275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23.07
5-th percentile30.93175
Q138.687499
median49.279999
Q359.599998
95-th percentile72.503001
Maximum85.269997
Range62.199997
Interquartile range (IQR)20.912499

Descriptive statistics

Standard deviation13.314377
Coefficient of variation (CV)0.26494837
Kurtosis-0.70992884
Mean50.252724
Median Absolute Deviation (MAD)10.512501
Skewness0.2975514
Sum227544.34
Variance177.27264
MonotonicityNot monotonic
2025-02-02T11:48:37.849149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.86000061 6
 
0.1%
44.99000168 6
 
0.1%
44.29000092 5
 
0.1%
47.68999863 5
 
0.1%
58.54000092 5
 
0.1%
31.95999908 5
 
0.1%
55.22000122 5
 
0.1%
44.95000076 5
 
0.1%
46.75 5
 
0.1%
43.65999985 5
 
0.1%
Other values (3129) 4476
98.9%
ValueCountFrequency (%)
23.06999969 1
< 0.1%
24.19000053 1
< 0.1%
25.04500008 1
< 0.1%
25.12999916 1
< 0.1%
25.70499992 1
< 0.1%
26.22999954 1
< 0.1%
26.68000031 1
< 0.1%
26.74500084 1
< 0.1%
26.96999931 1
< 0.1%
27.01000023 1
< 0.1%
ValueCountFrequency (%)
85.26999664 1
< 0.1%
85.12999725 1
< 0.1%
84.95999908 1
< 0.1%
84.73999786 1
< 0.1%
83.72000122 1
< 0.1%
83.58999634 1
< 0.1%
83.36000061 2
< 0.1%
83.31999969 1
< 0.1%
83.15000153 1
< 0.1%
83.05000305 1
< 0.1%

close
Real number (ℝ)

High correlation 

Distinct3115
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.754321
Minimum25.27
Maximum85.849998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.8 KiB
2025-02-02T11:48:38.027152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25.27
5-th percentile31.2185
Q139.026249
median49.790001
Q360.389999
95-th percentile73.189497
Maximum85.849998
Range60.579998
Interquartile range (IQR)21.36375

Descriptive statistics

Standard deviation13.434112
Coefficient of variation (CV)0.26468903
Kurtosis-0.7186049
Mean50.754321
Median Absolute Deviation (MAD)10.684999
Skewness0.29930615
Sum229815.57
Variance180.47537
MonotonicityNot monotonic
2025-02-02T11:48:38.283702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.70000076 7
 
0.2%
48.45000076 6
 
0.1%
49.45999908 5
 
0.1%
52.83000183 5
 
0.1%
59 5
 
0.1%
47.04999924 5
 
0.1%
47.88999939 5
 
0.1%
67.48000336 5
 
0.1%
56.75 5
 
0.1%
45.75 5
 
0.1%
Other values (3105) 4475
98.8%
ValueCountFrequency (%)
25.27000046 1
< 0.1%
25.69000053 1
< 0.1%
26.72999954 1
< 0.1%
26.89999962 1
< 0.1%
27.11499977 1
< 0.1%
27.25499916 1
< 0.1%
27.29000092 1
< 0.1%
27.30999947 1
< 0.1%
27.32999992 1
< 0.1%
27.48999977 1
< 0.1%
ValueCountFrequency (%)
85.84999847 1
< 0.1%
85.80999756 1
< 0.1%
85.54000092 1
< 0.1%
85.29000092 1
< 0.1%
84.95999908 1
< 0.1%
84.69999695 1
< 0.1%
84.34999847 1
< 0.1%
84.09999847 1
< 0.1%
84.02999878 1
< 0.1%
83.98000336 1
< 0.1%

volume
Real number (ℝ)

Distinct4293
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2378250.4
Minimum204200
Maximum32488300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.8 KiB
2025-02-02T11:48:38.497921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum204200
5-th percentile628300
Q11043075
median2030550
Q33208450
95-th percentile5340590
Maximum32488300
Range32284100
Interquartile range (IQR)2165375

Descriptive statistics

Standard deviation1743382.6
Coefficient of variation (CV)0.73305257
Kurtosis33.645917
Mean2378250.4
Median Absolute Deviation (MAD)1043350
Skewness3.2313997
Sum1.0768718 × 1010
Variance3.0393829 × 1012
MonotonicityNot monotonic
2025-02-02T11:48:38.711046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
883200 3
 
0.1%
1905000 3
 
0.1%
2401600 3
 
0.1%
2441600 3
 
0.1%
886800 3
 
0.1%
1144000 3
 
0.1%
848800 3
 
0.1%
1063200 3
 
0.1%
924000 2
 
< 0.1%
3198200 2
 
< 0.1%
Other values (4283) 4500
99.4%
ValueCountFrequency (%)
204200 1
< 0.1%
215000 1
< 0.1%
238200 1
< 0.1%
284300 1
< 0.1%
303200 1
< 0.1%
305800 1
< 0.1%
309800 1
< 0.1%
319000 1
< 0.1%
322900 1
< 0.1%
358700 1
< 0.1%
ValueCountFrequency (%)
32488300 1
< 0.1%
27589200 1
< 0.1%
17653100 1
< 0.1%
17390300 1
< 0.1%
14431000 1
< 0.1%
13528800 1
< 0.1%
11286200 1
< 0.1%
11232600 1
< 0.1%
11172400 1
< 0.1%
10797300 1
< 0.1%

dividend
Real number (ℝ)

Zeros 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0039333039
Minimum0
Maximum0.42
Zeros4456
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size70.8 KiB
2025-02-02T11:48:38.877769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.42
Range0.42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.032711725
Coefficient of variation (CV)8.3166025
Kurtosis84.390049
Mean0.0039333039
Median Absolute Deviation (MAD)0
Skewness8.9584869
Sum17.81
Variance0.001070057
MonotonicityNot monotonic
2025-02-02T11:48:39.011257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 4456
98.4%
0.26 8
 
0.2%
0.28 8
 
0.2%
0.42 4
 
0.1%
0.3 4
 
0.1%
0.24 4
 
0.1%
0.22 4
 
0.1%
0.14 4
 
0.1%
0.12 4
 
0.1%
0.095 4
 
0.1%
Other values (9) 28
 
0.6%
ValueCountFrequency (%)
0 4456
98.4%
0.095 4
 
0.1%
0.12 4
 
0.1%
0.14 4
 
0.1%
0.18 3
 
0.1%
0.195 3
 
0.1%
0.205 4
 
0.1%
0.215 4
 
0.1%
0.22 4
 
0.1%
0.225 1
 
< 0.1%
ValueCountFrequency (%)
0.42 4
0.1%
0.4 4
0.1%
0.33 4
0.1%
0.32 1
 
< 0.1%
0.3 4
0.1%
0.28 8
0.2%
0.27 4
0.1%
0.26 8
0.2%
0.24 4
0.1%
0.225 1
 
< 0.1%

Ticker
Categorical

Uniform 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
AFL
2264 
AOS
2264 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13584
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFL
2nd rowAFL
3rd rowAFL
4th rowAFL
5th rowAFL

Common Values

ValueCountFrequency (%)
AFL 2264
50.0%
AOS 2264
50.0%

Length

2025-02-02T11:48:39.144739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T11:48:39.243894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
afl 2264
50.0%
aos 2264
50.0%

Most occurring characters

ValueCountFrequency (%)
A 4528
33.3%
F 2264
16.7%
L 2264
16.7%
O 2264
16.7%
S 2264
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4528
33.3%
F 2264
16.7%
L 2264
16.7%
O 2264
16.7%
S 2264
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4528
33.3%
F 2264
16.7%
L 2264
16.7%
O 2264
16.7%
S 2264
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4528
33.3%
F 2264
16.7%
L 2264
16.7%
O 2264
16.7%
S 2264
16.7%

split_ratio
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
0.0
4526 
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13584
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4526
> 99.9%
2.0 2
 
< 0.1%

Length

2025-02-02T11:48:39.361538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T11:48:39.444999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4526
> 99.9%
2.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 9054
66.7%
. 4528
33.3%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9054
66.7%
. 4528
33.3%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9054
66.7%
. 4528
33.3%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9054
66.7%
. 4528
33.3%
2 2
 
< 0.1%

RSI
Real number (ℝ)

High correlation 

Distinct4503
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.929239
Minimum13.29602
Maximum90.636211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.8 KiB
2025-02-02T11:48:39.577375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13.29602
5-th percentile30.674618
Q145.308123
median53.891015
Q361.413131
95-th percentile71.803698
Maximum90.636211
Range77.340191
Interquartile range (IQR)16.105008

Descriptive statistics

Standard deviation12.240215
Coefficient of variation (CV)0.23125621
Kurtosis0.03738878
Mean52.929239
Median Absolute Deviation (MAD)8.0090044
Skewness-0.37579156
Sum239663.59
Variance149.82286
MonotonicityNot monotonic
2025-02-02T11:48:39.761890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.83521356 2
 
< 0.1%
61.91625861 2
 
< 0.1%
56.58007644 2
 
< 0.1%
34.53238033 2
 
< 0.1%
61.31194907 2
 
< 0.1%
43.91384127 2
 
< 0.1%
61.51758426 2
 
< 0.1%
47.09483431 2
 
< 0.1%
54.94215186 2
 
< 0.1%
69.9783327 2
 
< 0.1%
Other values (4493) 4508
99.6%
ValueCountFrequency (%)
13.29601996 1
< 0.1%
14.62133641 1
< 0.1%
14.64683755 1
< 0.1%
14.69867964 1
< 0.1%
14.76339729 1
< 0.1%
15.19398467 1
< 0.1%
15.24019898 1
< 0.1%
15.30287616 1
< 0.1%
15.30594927 1
< 0.1%
15.31561684 1
< 0.1%
ValueCountFrequency (%)
90.63621054 1
< 0.1%
85.72174905 1
< 0.1%
84.68255155 1
< 0.1%
84.31884476 1
< 0.1%
84.09513685 1
< 0.1%
81.80653479 1
< 0.1%
81.72659059 1
< 0.1%
81.72605378 1
< 0.1%
81.36096246 1
< 0.1%
81.33409306 1
< 0.1%

MACD
Real number (ℝ)

High correlation  Unique 

Distinct4528
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020294983
Minimum-27.835975
Maximum4.069193
Zeros0
Zeros (%)0.0%
Negative1639
Negative (%)36.2%
Memory size70.8 KiB
2025-02-02T11:48:39.978142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-27.835975
5-th percentile-1.6902014
Q1-0.23357389
median0.21962252
Q30.65278407
95-th percentile1.5516738
Maximum4.069193
Range31.905168
Interquartile range (IQR)0.88635796

Descriptive statistics

Standard deviation1.9120201
Coefficient of variation (CV)94.211468
Kurtosis110.30767
Mean0.020294983
Median Absolute Deviation (MAD)0.44482658
Skewness-9.0013935
Sum91.895682
Variance3.6558209
MonotonicityNot monotonic
2025-02-02T11:48:40.178938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.505608211 1
 
< 0.1%
0.2574555118 1
 
< 0.1%
0.3579908085 1
 
< 0.1%
0.2685619078 1
 
< 0.1%
0.231966612 1
 
< 0.1%
0.211410625 1
 
< 0.1%
0.2197716538 1
 
< 0.1%
0.2527012339 1
 
< 0.1%
0.232287936 1
 
< 0.1%
0.5092631801 1
 
< 0.1%
Other values (4518) 4518
99.8%
ValueCountFrequency (%)
-27.83597487 1
< 0.1%
-27.83155603 1
< 0.1%
-27.49377422 1
< 0.1%
-27.49374615 1
< 0.1%
-26.92179397 1
< 0.1%
-26.66415626 1
< 0.1%
-26.16247273 1
< 0.1%
-25.22967369 1
< 0.1%
-25.22394502 1
< 0.1%
-24.20955496 1
< 0.1%
ValueCountFrequency (%)
4.069192957 1
< 0.1%
4.031353098 1
< 0.1%
4.013501867 1
< 0.1%
3.967731073 1
< 0.1%
3.9074601 1
< 0.1%
3.899791272 1
< 0.1%
3.79265772 1
< 0.1%
3.774199302 1
< 0.1%
3.628004087 1
< 0.1%
3.557362797 1
< 0.1%

MACD_Signal
Real number (ℝ)

High correlation  Unique 

Distinct4528
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024819782
Minimum-24.190756
Maximum4.8159039
Zeros0
Zeros (%)0.0%
Negative1644
Negative (%)36.3%
Memory size70.8 KiB
2025-02-02T11:48:40.362291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-24.190756
5-th percentile-1.5566032
Q1-0.20604501
median0.21105092
Q30.62914385
95-th percentile1.4633429
Maximum4.8159039
Range29.00666
Interquartile range (IQR)0.83518886

Descriptive statistics

Standard deviation1.7736426
Coefficient of variation (CV)71.460846
Kurtosis95.511234
Mean0.024819782
Median Absolute Deviation (MAD)0.41803111
Skewness-8.3549674
Sum112.38397
Variance3.1458081
MonotonicityNot monotonic
2025-02-02T11:48:40.614843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8473552249 1
 
< 0.1%
0.2745979242 1
 
< 0.1%
0.2724571615 1
 
< 0.1%
0.2510737497 1
 
< 0.1%
0.2467017102 1
 
< 0.1%
0.2503854847 1
 
< 0.1%
0.2601291997 1
 
< 0.1%
0.2702185861 1
 
< 0.1%
0.2788835273 1
 
< 0.1%
0.3478905125 1
 
< 0.1%
Other values (4518) 4518
99.8%
ValueCountFrequency (%)
-24.19075618 1
< 0.1%
-24.18605649 1
< 0.1%
-23.97791451 1
< 0.1%
-23.92658435 1
< 0.1%
-23.55435733 1
< 0.1%
-23.36761226 1
< 0.1%
-22.97019774 1
< 0.1%
-22.47906684 1
< 0.1%
-22.26364455 1
< 0.1%
-21.4662739 1
< 0.1%
ValueCountFrequency (%)
4.815903944 1
< 0.1%
3.71869115 1
< 0.1%
3.704814112 1
< 0.1%
3.700553737 1
< 0.1%
3.656069822 1
< 0.1%
3.632690142 1
< 0.1%
3.578154509 1
< 0.1%
3.526703415 1
< 0.1%
3.464854862 1
< 0.1%
3.365462324 1
< 0.1%

MACD_Diff
Real number (ℝ)

High correlation  Unique 

Distinct4528
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0045247989
Minimum-14.646805
Maximum3.8506333
Zeros0
Zeros (%)0.0%
Negative2232
Negative (%)49.3%
Memory size70.8 KiB
2025-02-02T11:48:40.846138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-14.646805
5-th percentile-0.48304015
Q1-0.13384427
median0.003680415
Q30.15292904
95-th percentile0.49662792
Maximum3.8506333
Range18.497438
Interquartile range (IQR)0.28677331

Descriptive statistics

Standard deviation0.66045432
Coefficient of variation (CV)-145.96324
Kurtosis245.98633
Mean-0.0045247989
Median Absolute Deviation (MAD)0.14436271
Skewness-12.092083
Sum-20.488289
Variance0.43619991
MonotonicityNot monotonic
2025-02-02T11:48:41.046285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6582529858 1
 
< 0.1%
-0.01714241239 1
 
< 0.1%
0.08553364707 1
 
< 0.1%
0.0174881581 1
 
< 0.1%
-0.01473509816 1
 
< 0.1%
-0.03897485976 1
 
< 0.1%
-0.04035754582 1
 
< 0.1%
-0.01751735218 1
 
< 0.1%
-0.04659559129 1
 
< 0.1%
0.1613726676 1
 
< 0.1%
Other values (4518) 4518
99.8%
ValueCountFrequency (%)
-14.646805 1
< 0.1%
-14.2043556 1
< 0.1%
-13.97832366 1
< 0.1%
-13.1307633 1
< 0.1%
-11.69900978 1
< 0.1%
-11.6521967 1
< 0.1%
-9.985429274 1
< 0.1%
-8.262126396 1
< 0.1%
-7.134422045 1
< 0.1%
-6.606166037 1
< 0.1%
ValueCountFrequency (%)
3.850633321 1
< 0.1%
3.849922447 1
< 0.1%
3.800253948 1
< 0.1%
3.79347023 1
< 0.1%
3.73735866 1
< 0.1%
3.667824616 1
< 0.1%
3.642252269 1
< 0.1%
3.532894254 1
< 0.1%
3.458123471 1
< 0.1%
3.402073329 1
< 0.1%

Bollinger_Upper
Real number (ℝ)

High correlation 

Distinct4525
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.611084
Minimum24.622363
Maximum210.27718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.8 KiB
2025-02-02T11:48:41.302651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24.622363
5-th percentile32.349818
Q141.207996
median52.512461
Q363.681874
95-th percentile77.270657
Maximum210.27718
Range185.65482
Interquartile range (IQR)22.473878

Descriptive statistics

Standard deviation16.225735
Coefficient of variation (CV)0.30265634
Kurtosis18.303953
Mean53.611084
Median Absolute Deviation (MAD)11.252733
Skewness2.3861268
Sum242750.99
Variance263.27446
MonotonicityNot monotonic
2025-02-02T11:48:41.512066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.43774218 2
 
< 0.1%
53.30448114 2
 
< 0.1%
42.35735884 2
 
< 0.1%
62.59968585 1
 
< 0.1%
63.20940252 1
 
< 0.1%
63.0059128 1
 
< 0.1%
62.6870176 1
 
< 0.1%
62.39107375 1
 
< 0.1%
62.29836924 1
 
< 0.1%
62.35546347 1
 
< 0.1%
Other values (4515) 4515
99.7%
ValueCountFrequency (%)
24.62236301 1
< 0.1%
26.83786518 1
< 0.1%
28.49178935 1
< 0.1%
29.23133271 1
< 0.1%
29.24904127 1
< 0.1%
29.29860825 1
< 0.1%
29.34727613 1
< 0.1%
29.36075559 1
< 0.1%
29.36839679 1
< 0.1%
29.37138135 1
< 0.1%
ValueCountFrequency (%)
210.2771781 1
< 0.1%
209.5500229 1
< 0.1%
209.1236565 1
< 0.1%
207.241251 1
< 0.1%
206.4358593 1
< 0.1%
203.711379 1
< 0.1%
201.0370009 1
< 0.1%
199.138725 1
< 0.1%
193.3008701 1
< 0.1%
193.0513703 1
< 0.1%

Bollinger_Lower
Real number (ℝ)

High correlation 

Distinct4525
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.883242
Minimum-44.782791
Maximum80.703888
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)0.3%
Memory size70.8 KiB
2025-02-02T11:48:41.680108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-44.782791
5-th percentile29.668363
Q137.101788
median47.442636
Q357.185116
95-th percentile69.517373
Maximum80.703888
Range125.48668
Interquartile range (IQR)20.083328

Descriptive statistics

Standard deviation13.543005
Coefficient of variation (CV)0.28283391
Kurtosis2.8729962
Mean47.883242
Median Absolute Deviation (MAD)9.9256809
Skewness-0.38705186
Sum216815.32
Variance183.41298
MonotonicityNot monotonic
2025-02-02T11:48:41.881459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.60375753 2
 
< 0.1%
50.83251875 2
 
< 0.1%
41.74664125 2
 
< 0.1%
60.34131489 1
 
< 0.1%
60.06559786 1
 
< 0.1%
60.16708754 1
 
< 0.1%
60.31598266 1
 
< 0.1%
60.45292649 1
 
< 0.1%
60.48463133 1
 
< 0.1%
60.45753702 1
 
< 0.1%
Other values (4515) 4515
99.7%
ValueCountFrequency (%)
-44.7827911 1
< 0.1%
-44.34874493 1
< 0.1%
-43.65151191 1
< 0.1%
-42.22119331 1
< 0.1%
-39.74519858 1
< 0.1%
-39.20047805 1
< 0.1%
-34.93386956 1
< 0.1%
-32.25002475 1
< 0.1%
-29.58972502 1
< 0.1%
-23.07387898 1
< 0.1%
ValueCountFrequency (%)
80.70388821 1
< 0.1%
80.69104289 1
< 0.1%
80.66774672 1
< 0.1%
80.63567024 1
< 0.1%
80.58118352 1
< 0.1%
80.56133702 1
< 0.1%
80.52669791 1
< 0.1%
80.51347203 1
< 0.1%
80.5125078 1
< 0.1%
80.44083938 1
< 0.1%

Bollinger_Middle
Real number (ℝ)

High correlation 

Distinct4511
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.747163
Minimum19.365
Maximum130.30525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.8 KiB
2025-02-02T11:48:42.084524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.365
5-th percentile31.231775
Q138.985625
median49.73
Q360.292875
95-th percentile72.789925
Maximum130.30525
Range110.94025
Interquartile range (IQR)21.30725

Descriptive statistics

Standard deviation13.548404
Coefficient of variation (CV)0.26697854
Kurtosis0.067098651
Mean50.747163
Median Absolute Deviation (MAD)10.640625
Skewness0.42501119
Sum229783.16
Variance183.55925
MonotonicityNot monotonic
2025-02-02T11:48:42.280620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.72049999 2
 
< 0.1%
59.65149975 2
 
< 0.1%
53.75650005 2
 
< 0.1%
53.28899975 2
 
< 0.1%
52.06849995 2
 
< 0.1%
42.05200005 2
 
< 0.1%
53.26150017 2
 
< 0.1%
57.49899979 2
 
< 0.1%
34.5647501 2
 
< 0.1%
45.49000053 2
 
< 0.1%
Other values (4501) 4508
99.6%
ValueCountFrequency (%)
19.36500015 1
< 0.1%
19.95300016 1
< 0.1%
20.52975016 1
< 0.1%
21.11575012 1
< 0.1%
21.70475016 1
< 0.1%
22.25950022 1
< 0.1%
22.81425028 1
< 0.1%
23.35375032 1
< 0.1%
23.82600031 1
< 0.1%
24.27850037 1
< 0.1%
ValueCountFrequency (%)
130.3052499 1
< 0.1%
125.7524999 1
< 0.1%
121.198 1
< 0.1%
116.7315001 1
< 0.1%
111.7232503 1
< 0.1%
106.6650003 1
< 0.1%
101.3377501 1
< 0.1%
95.84450016 1
< 0.1%
90.31875 1
< 0.1%
84.77449999 1
< 0.1%

Interactions

2025-02-02T11:48:29.303165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:00.128878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:02.141624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:04.149038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:06.204322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:10.025196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:12.328436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:14.294726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:16.392419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:19.091931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:21.296694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:23.765308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:26.267319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:29.585183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:00.295828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:02.285548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:04.290980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:06.334167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:10.187498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:12.484131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:14.457679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:16.625957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:19.294791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:21.446718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:23.944313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:26.434133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:29.932071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:00.429111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:02.433022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:04.432520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:06.501357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:10.371787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:12.634197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:14.623826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:16.797239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:19.493858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:21.620443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:24.117527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:26.621058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:30.216247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:00.583779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:02.580107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:04.577607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:06.766012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:10.582568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:12.756059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:14.769883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:16.950329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:19.663815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:21.788745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:24.281121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:26.786147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:30.514774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:00.715066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:02.723453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:04.723965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:06.998728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:10.756512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:12.924533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:14.944278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:17.257757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:19.811964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:21.942403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:24.447422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:26.962302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:30.828308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:00.879582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:02.899622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:04.900515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:07.214768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:10.912418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:13.066336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:15.108185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:17.617079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:19.978840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:22.256351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:24.615979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:27.169652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:31.108706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:01.012475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:03.031335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:05.069430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:07.440676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:11.126471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:13.197632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:15.256573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:17.815367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:20.126956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:22.514362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:24.781567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:27.338252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:31.533218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:01.163156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:03.206717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:05.232818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:07.686261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:11.288242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:13.337348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:15.443748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:18.006565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:20.262373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:22.707719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:24.956472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:27.537327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:32.122210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:01.310443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:03.363982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:05.392110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:07.933226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:11.460923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:13.491136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:15.606379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:18.159746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:20.432344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:22.896512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:25.193200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:27.791612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:32.544208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:01.480020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:03.516166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:05.550164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:08.179957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:11.605178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:13.623717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:15.778939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:18.298129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:20.607324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:23.087342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:25.355385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:28.002237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:32.958262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:01.655571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:03.681816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:05.729234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:08.402436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:11.788072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:13.792450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:15.941106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:18.478494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:20.774879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:23.248373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:25.567943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:28.322352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-02T11:48:01.822412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:03.841666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:05.897788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:08.584042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:12.019996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:13.973414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:16.109207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:18.677727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:20.964403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:23.431899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:25.771279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:28.519966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:33.518927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:01.963789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:03.982168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:06.050579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:09.857905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:12.173172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:14.130520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:16.262166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:18.855679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:21.146588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:23.598478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:26.046098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T11:48:28.779982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-02T11:48:42.447419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Bollinger_LowerBollinger_MiddleBollinger_UpperMACDMACD_DiffMACD_SignalRSITickerclosedividendhighlowopensplit_ratiovolume
Bollinger_Lower1.0000.9770.9510.162-0.1070.2060.0000.3020.983-0.0000.9830.9840.9840.000-0.437
Bollinger_Middle0.9771.0000.9900.136-0.1170.177-0.0310.2780.970-0.0010.9710.9700.9710.000-0.452
Bollinger_Upper0.9510.9901.0000.119-0.1060.155-0.0480.2810.955-0.0020.9570.9530.9560.000-0.447
MACD0.1620.1360.1191.0000.2480.9420.8180.1640.2430.0140.2380.2460.2410.000-0.100
MACD_Diff-0.107-0.117-0.1060.2481.000-0.0220.5250.1710.010-0.0000.0040.0070.0010.000-0.078
MACD_Signal0.2060.1770.1550.942-0.0221.0000.6650.1980.2490.0120.2460.2530.2500.000-0.095
RSI0.000-0.031-0.0480.8180.5250.6651.0000.1190.1030.0030.0910.1020.0900.000-0.036
Ticker0.3020.2780.2810.1640.1710.1980.1191.0000.2890.0600.2990.2610.2830.0000.465
close0.9830.9700.9550.2430.0100.2490.1030.2891.0000.0020.9990.9990.9990.000-0.454
dividend-0.000-0.001-0.0020.014-0.0000.0120.0030.0600.0021.0000.003-0.0010.0000.0000.041
high0.9830.9710.9570.2380.0040.2460.0910.2990.9990.0031.0000.9990.9990.000-0.451
low0.9840.9700.9530.2460.0070.2530.1020.2610.999-0.0010.9991.0000.9990.000-0.456
open0.9840.9710.9560.2410.0010.2500.0900.2830.9990.0000.9990.9991.0000.000-0.453
split_ratio0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
volume-0.437-0.452-0.447-0.100-0.078-0.095-0.0360.465-0.4540.041-0.451-0.456-0.4530.0001.000

Missing values

2025-02-02T11:48:33.962538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-02T11:48:34.356837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

dateopenhighlowclosevolumedividendTickersplit_ratioRSIMACDMACD_SignalMACD_DiffBollinger_UpperBollinger_LowerBollinger_Middle
181122015-01-0230.75000030.80500030.33000030.54000128460000.0AFL0.090.6362111.5056080.8473550.65825324.62236314.10763719.36500
181132015-01-0530.21999930.27500029.62000129.74000047570000.0AFL0.085.7217492.1090821.0997011.00938226.83786513.06813519.95300
181142015-01-0629.70500029.77500029.39500029.47500058190000.0AFL0.084.0951372.5367151.3871031.14961128.49178912.56771120.52975
181152015-01-0729.72500029.98000029.54500029.66000036534000.0AFL0.084.3188452.8576041.6812041.17640029.91071512.32078521.11575
181162015-01-0829.87000130.16000029.73000029.95000145876000.0AFL0.084.6825523.0995821.9648791.13470231.18299012.22651021.70475
181172015-01-0930.03000130.08499929.27500029.31500159936000.0AFL0.080.2911783.2031872.2125410.99064632.14689312.37210822.25950
181182015-01-1229.30500029.37500028.96999929.14500035732000.0AFL0.079.1084383.2342942.4168910.81740332.93687812.69162322.81425
181192015-01-1329.34499929.53000128.79999929.00000033410000.0AFL0.078.0523213.2102412.5755610.63468033.58684513.12065623.35375
181202015-01-1428.60000028.98500128.48500128.77500043036000.0AFL0.076.3490203.1368642.6878220.44904234.14414013.50786023.82600
181212015-01-1528.67499929.13500028.65000028.68000045796000.0AFL0.075.5988803.0360482.7574670.27858134.61464913.94235224.27850
dateopenhighlowclosevolumedividendTickersplit_ratioRSIMACDMACD_SignalMACD_DiffBollinger_UpperBollinger_LowerBollinger_Middle
45182023-12-1579.73000380.37999778.98000379.19999729104000.0AOS0.066.4758221.8773481.7698320.10751580.24526173.95973877.102499
45192023-12-1879.33999679.86000178.86000179.3899999263000.0AOS0.067.1581511.8347161.7828090.05190780.52843573.97756477.252999
45202023-12-1979.66999880.93000079.66999880.9000029136000.0AOS0.072.0303591.9008621.8064200.09444281.08386073.90113977.492500
45212023-12-2080.69999781.34999880.22000180.2300037901000.0AOS0.067.2622071.8775771.8206510.05692681.40915674.01184377.710500
45222023-12-2181.02999981.57000080.82000081.5599986216000.0AOS0.071.3206931.9440331.8453280.09870581.95063674.03836377.994500
45232023-12-2281.94999782.02999981.23999881.7300034494000.0AOS0.071.8019011.9875071.8737630.11374482.44306974.11793078.280500
45242023-12-2681.69999782.05999881.41999881.8499984204000.0AOS0.072.1570132.0084911.9007090.10778282.87050774.27949278.575000
45252023-12-2781.98000382.40000281.69000282.1600044417000.0AOS0.073.0995422.0267721.9259220.10085083.18596974.66803178.927000
45262023-12-2881.93000082.44000281.83000282.2399984528000.0AOS0.073.3502372.0243791.9456130.07876683.39435675.17364479.284000
45272023-12-2982.02999982.76999782.02999982.4400026109000.0AOS0.074.0025902.0153891.9595680.05582183.55032475.72567679.638000